Many excavation and loading tasks require equipment operators to simultaneously coordinate the motion of multiple machine links, in order to achieve complex tool trajectories. Efficiency, work quality, and machine wear, can all be affected by the skill and attentiveness of the operator. A software-based kinematics reconfiguration system is described in Danko, G. (2001). Operator control architecture, Proceedings, The 4th Symposium on Computer Applications in the Mineral Industries, Tampere, Finland, 253-265. (“Danko (2001)” herein) to assist the operator with various tasks. As described in Danko Danko (2001), the operator can select a job-specific, virtual motion kinematics, to be followed by the real machine, under automatic control, and can control the machine in this virtual space.
The implementation of a kinematics transformation with a real machine to follow is a difficult task using sensors and actuators which are usually imperfect. Hydraulic control of heavy machinery adds another difficulty related to the efficiency of the actuators. For energy loss reduction, mobile hydraulics systems generally employ simplified control loops which have higher energy efficiencies than some other control loops, for example such as may be employed for hydraulic robots. However employing simplified control loops can compromise response time and precision.
Manual control of such mobile machines with imperfect hydraulic circuits can work surprisingly well. Compensation for motion errors and response delays can be learned by an operator through training and work experience. Some skilled, experienced operators of heavy hydraulic machinery can perform fine-tuned control tasks with ease and confidence. Accordingly, it would be desirable to provide control methods and systems for such machines that can more efficiently assist the end effector to follow a desired trajectory.
One useful approach is to provide a simple, mathematical model of the machine dynamics and control response during real-time operation and to incorporate the dynamic model in the control loop of the imperfect machine.
System identification in hydraulic machine control systems is addressed in U.S. Patent Application Publication No. 2007/0168100 to Danko (“Danko '100” herein) the entire disclosure of which is incorporated by reference herein.
Some of the difficulties that may arise in providing suitable control methods and systems include the difficulty of adapting to time-dependent variations of the machine itself. A control system's range of adaptation is usually limited, and the control system's adaptation ability may be overwhelmed in case of large changes in factors such as the external load on the machine, unknown disturbances due to the mass and inertia of the load-bearing links and so on. Orders of magnitude variation in external load disturbances are not unusual during some excavation and loading operations in the construction and mining industries.
For these and other reasons, known control methods and systems can attempt to provide great flexibility in the adaptability of the control system parameters to allow for unknown, possibly unlimited and arbitrary ranges of variation in such factors.
Industrial robots can be sometimes controlled based on a pre-planned trajectory which can be described in terms of the position and orientation of the end-effector (by means of start and end points, as well as intermediate via points). The velocity of the end-effector may also be specified at various points along the path. Trajectory planning involves calculation of a continuous desired path (described in terms of position, velocity and acceleration) which satisfies the specified constraints. Joint torques can be calculated based on the desired acceleration using the inverse kinematics of the manipulator.
Differential control architecture such as that described and claimed in U.S. Patent Application Publication No. 2004/0267404 to Danko (“Danko '404” herein), the disclosure of which is incorporated by reference herein, allows an operator to perform the trajectory planning in real-time by recognizing that a manual machine is often velocity controlled. Position is controlled implicitly by integration of the machine velocity.
Danko '404 describes and claims a differential control architecture wherein as-built motion kinematics of the machine can be transformed into an as-desired, virtual motion kinematics. The operator can control a virtual, as-desired machine, while the real, as-built machine follows the movement of the virtual machine. In some embodiments of the described invention, the motion trajectory of the tool of the machine is not explicitly determined nor is employed for control of the machine. Position information (from measurement) can, if desired be employed, for the transformation of the machine kinematics from as-built to a virtual, as-desired machine. A kinematics transformation performed as described in Danko '404 can be useful but approximate owing to measured but imprecise tool position information in the calculation.
Danko '100 describes control architecture wherein tool position information from measurement is not required in the machine kinematics transformation. Instead, the calculated tool position of the virtual machine can be used for the kinematics transformation. This is because the tool motion and the kinematics of the real machine may not accurately follow the desired tool motion of the virtual machine. Embodiments of Danko '100 can compare a virtual, potentially errorless, desired reference tool position with the measured tool position. The velocity control signals to the actuators can then be modified by closed-loop feedback control, or other suitable means, in such a way that the difference between the measured real tool position and the reference tool position is reduced or minimized or eliminated. However, in some cases, the reference position derived from a kinematics model may not be achievable with a real machine having dynamic limitations.
Accordingly, it would be desirable to provide a control method and system for controlling a hydraulic machine which can adapt to the dynamic limitations of a real machine.
The foregoing description of background art may include insights, discoveries, understandings or disclosures, or associations together of disclosures, that were not known to the relevant art prior to the present invention but which were provided by the invention. Some such contributions of the invention may have been specifically pointed out herein, whereas other such contributions of the invention will be apparent from their context. Merely because a document may have been cited here, no admission is made that the field of the document, which may be quite different from that of the invention, is analogous to the field or fields of the present invention.